Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations3191
Missing cells0
Missing cells (%)0.0%
Duplicate rows481
Duplicate rows (%)15.1%
Total size in memory478.1 KiB
Average record size in memory153.4 B

Variable types

Categorical1
Numeric12

Alerts

Dataset has 481 (15.1%) duplicate rowsDuplicates
alcohol is highly overall correlated with densityHigh correlation
chlorides is highly overall correlated with density and 4 other fieldsHigh correlation
density is highly overall correlated with alcohol and 2 other fieldsHigh correlation
fixed acidity is highly overall correlated with chlorides and 2 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
residual sugar is highly overall correlated with typeHigh correlation
sulphates is highly overall correlated with typeHigh correlation
total sulfur dioxide is highly overall correlated with chlorides and 2 other fieldsHigh correlation
type is highly overall correlated with chlorides and 5 other fieldsHigh correlation
volatile acidity is highly overall correlated with chlorides and 1 other fieldsHigh correlation
citric acid has 140 (4.4%) zeros Zeros

Reproduction

Analysis started2024-10-30 19:33:42.336411
Analysis finished2024-10-30 19:33:55.576382
Duration13.24 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size178.9 KiB
Moscatel
1598 
Syrah
1593 

Length

Max length8
Median length8
Mean length6.5023504
Min length5

Characters and Unicode

Total characters20749
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMoscatel
2nd rowMoscatel
3rd rowMoscatel
4th rowMoscatel
5th rowMoscatel

Common Values

ValueCountFrequency (%)
Moscatel 1598
50.1%
Syrah 1593
49.9%

Length

2024-10-30T16:33:55.636653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-30T16:33:55.716801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
moscatel 1598
50.1%
syrah 1593
49.9%

Most occurring characters

ValueCountFrequency (%)
a 3191
15.4%
M 1598
7.7%
s 1598
7.7%
o 1598
7.7%
c 1598
7.7%
t 1598
7.7%
e 1598
7.7%
l 1598
7.7%
S 1593
7.7%
y 1593
7.7%
Other values (2) 3186
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3191
15.4%
M 1598
7.7%
s 1598
7.7%
o 1598
7.7%
c 1598
7.7%
t 1598
7.7%
e 1598
7.7%
l 1598
7.7%
S 1593
7.7%
y 1593
7.7%
Other values (2) 3186
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3191
15.4%
M 1598
7.7%
s 1598
7.7%
o 1598
7.7%
c 1598
7.7%
t 1598
7.7%
e 1598
7.7%
l 1598
7.7%
S 1593
7.7%
y 1593
7.7%
Other values (2) 3186
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3191
15.4%
M 1598
7.7%
s 1598
7.7%
o 1598
7.7%
c 1598
7.7%
t 1598
7.7%
e 1598
7.7%
l 1598
7.7%
S 1593
7.7%
y 1593
7.7%
Other values (2) 3186
15.4%

fixed acidity
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4250078
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.9 KiB
2024-10-30T16:33:55.806220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.6
Q16.4
median7
Q38
95-th percentile10.7
Maximum15.9
Range12.1
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.5984636
Coefficient of variation (CV)0.21528107
Kurtosis2.7575382
Mean7.4250078
Median Absolute Deviation (MAD)0.8
Skewness1.4700301
Sum23693.2
Variance2.555086
MonotonicityNot monotonic
2024-10-30T16:33:55.918778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 155
 
4.9%
6.8 151
 
4.7%
6.4 144
 
4.5%
6.7 119
 
3.7%
7.2 116
 
3.6%
7 114
 
3.6%
6 114
 
3.6%
7.1 111
 
3.5%
6.9 103
 
3.2%
6.5 101
 
3.2%
Other values (90) 1963
61.5%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4.4 3
 
0.1%
4.6 1
 
< 0.1%
4.7 6
 
0.2%
4.8 7
 
0.2%
4.9 5
 
0.2%
5 19
0.6%
5.1 14
0.4%
5.2 16
0.5%
ValueCountFrequency (%)
15.9 1
< 0.1%
15.6 2
0.1%
15.5 2
0.1%
15 2
0.1%
14.3 1
< 0.1%
14 1
< 0.1%
13.8 1
< 0.1%
13.7 2
0.1%
13.5 1
< 0.1%
13.4 1
< 0.1%

volatile acidity
Real number (ℝ)

High correlation 

Distinct171
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40464275
Minimum0.085
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.9 KiB
2024-10-30T16:33:56.026060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.085
5-th percentile0.17
Q10.26
median0.36
Q30.53
95-th percentile0.7475
Maximum1.58
Range1.495
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.18965419
Coefficient of variation (CV)0.4686954
Kurtosis1.0761404
Mean0.40464275
Median Absolute Deviation (MAD)0.12
Skewness0.99709392
Sum1291.215
Variance0.035968712
MonotonicityNot monotonic
2024-10-30T16:33:56.136425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 120
 
3.8%
0.24 102
 
3.2%
0.22 98
 
3.1%
0.27 89
 
2.8%
0.26 87
 
2.7%
0.32 87
 
2.7%
0.3 85
 
2.7%
0.31 85
 
2.7%
0.36 79
 
2.5%
0.38 73
 
2.3%
Other values (161) 2286
71.6%
ValueCountFrequency (%)
0.085 1
 
< 0.1%
0.09 1
 
< 0.1%
0.105 4
 
0.1%
0.11 5
 
0.2%
0.12 10
 
0.3%
0.13 8
 
0.3%
0.14 15
 
0.5%
0.145 2
 
0.1%
0.15 31
1.0%
0.16 48
1.5%
ValueCountFrequency (%)
1.58 1
< 0.1%
1.33 2
0.1%
1.24 1
< 0.1%
1.185 1
< 0.1%
1.18 1
< 0.1%
1.13 1
< 0.1%
1.115 1
< 0.1%
1.1 1
< 0.1%
1.09 1
< 0.1%
1.07 1
< 0.1%

citric acid
Real number (ℝ)

Zeros 

Distinct83
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28797242
Minimum0
Maximum1
Zeros140
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size178.9 KiB
2024-10-30T16:33:56.249199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.2
median0.28
Q30.37
95-th percentile0.56
Maximum1
Range1
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.15735966
Coefficient of variation (CV)0.54644004
Kurtosis0.42839103
Mean0.28797242
Median Absolute Deviation (MAD)0.08
Skewness0.3139611
Sum918.92
Variance0.024762063
MonotonicityNot monotonic
2024-10-30T16:33:56.359619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 155
 
4.9%
0.3 143
 
4.5%
0 140
 
4.4%
0.26 130
 
4.1%
0.32 126
 
3.9%
0.27 123
 
3.9%
0.24 115
 
3.6%
0.29 111
 
3.5%
0.25 91
 
2.9%
0.33 87
 
2.7%
Other values (73) 1970
61.7%
ValueCountFrequency (%)
0 140
4.4%
0.01 37
 
1.2%
0.02 52
 
1.6%
0.03 30
 
0.9%
0.04 32
 
1.0%
0.05 21
 
0.7%
0.06 26
 
0.8%
0.07 22
 
0.7%
0.08 33
 
1.0%
0.09 37
 
1.2%
ValueCountFrequency (%)
1 2
 
0.1%
0.91 2
 
0.1%
0.86 1
 
< 0.1%
0.82 1
 
< 0.1%
0.79 2
 
0.1%
0.78 2
 
0.1%
0.76 3
0.1%
0.75 1
 
< 0.1%
0.74 5
0.2%
0.73 4
0.1%

residual sugar
Real number (ℝ)

High correlation 

Distinct234
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5203855
Minimum0.7
Maximum26.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.9 KiB
2024-10-30T16:33:56.463245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.2
Q11.9
median2.5
Q36.05
95-th percentile13.8
Maximum26.05
Range25.35
Interquartile range (IQR)4.15

Descriptive statistics

Standard deviation4.1507944
Coefficient of variation (CV)0.91823905
Kurtosis2.1537261
Mean4.5203855
Median Absolute Deviation (MAD)0.9
Skewness1.6779611
Sum14424.55
Variance17.229094
MonotonicityNot monotonic
2024-10-30T16:33:56.569844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 187
 
5.9%
1.8 155
 
4.9%
2.2 144
 
4.5%
2.1 140
 
4.4%
1.9 139
 
4.4%
2.3 122
 
3.8%
2.4 104
 
3.3%
2.5 103
 
3.2%
1.6 96
 
3.0%
1.7 94
 
2.9%
Other values (224) 1907
59.8%
ValueCountFrequency (%)
0.7 2
 
0.1%
0.8 5
 
0.2%
0.9 16
 
0.5%
1 29
 
0.9%
1.1 52
1.6%
1.15 1
 
< 0.1%
1.2 74
2.3%
1.3 55
1.7%
1.4 84
2.6%
1.45 1
 
< 0.1%
ValueCountFrequency (%)
26.05 2
0.1%
22.6 1
 
< 0.1%
20.8 1
 
< 0.1%
20.3 1
 
< 0.1%
20.15 1
 
< 0.1%
19.95 1
 
< 0.1%
19.9 1
 
< 0.1%
19.5 1
 
< 0.1%
19.4 1
 
< 0.1%
19.3 3
0.1%

chlorides
Real number (ℝ)

High correlation 

Distinct192
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.066371357
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.9 KiB
2024-10-30T16:33:56.673696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.03
Q10.042
median0.058
Q30.08
95-th percentile0.114
Maximum0.611
Range0.602
Interquartile range (IQR)0.038

Descriptive statistics

Standard deviation0.042076945
Coefficient of variation (CV)0.6339624
Kurtosis41.614527
Mean0.066371357
Median Absolute Deviation (MAD)0.019
Skewness5.0140462
Sum211.791
Variance0.0017704693
MonotonicityNot monotonic
2024-10-30T16:33:56.784891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.036 76
 
2.4%
0.048 75
 
2.4%
0.044 74
 
2.3%
0.05 69
 
2.2%
0.08 66
 
2.1%
0.047 63
 
2.0%
0.042 61
 
1.9%
0.041 56
 
1.8%
0.076 55
 
1.7%
0.035 55
 
1.7%
Other values (182) 2541
79.6%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.012 2
 
0.1%
0.013 1
 
< 0.1%
0.014 2
 
0.1%
0.015 4
0.1%
0.016 1
 
< 0.1%
0.017 3
0.1%
0.018 4
0.1%
0.019 1
 
< 0.1%
0.02 6
0.2%
ValueCountFrequency (%)
0.611 1
 
< 0.1%
0.61 1
 
< 0.1%
0.467 1
 
< 0.1%
0.464 1
 
< 0.1%
0.422 1
 
< 0.1%
0.415 3
0.1%
0.414 2
0.1%
0.413 1
 
< 0.1%
0.403 1
 
< 0.1%
0.401 1
 
< 0.1%

free sulfur dioxide
Real number (ℝ)

High correlation 

Distinct99
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.528048
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.9 KiB
2024-10-30T16:33:56.893309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q112
median23
Q335
95-th percentile57
Maximum289
Range288
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.408059
Coefficient of variation (CV)0.68191893
Kurtosis17.506675
Mean25.528048
Median Absolute Deviation (MAD)11
Skewness2.0366479
Sum81460
Variance303.04051
MonotonicityNot monotonic
2024-10-30T16:33:56.997561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 148
 
4.6%
5 110
 
3.4%
15 98
 
3.1%
10 94
 
2.9%
17 91
 
2.9%
12 90
 
2.8%
16 83
 
2.6%
26 81
 
2.5%
29 79
 
2.5%
7 79
 
2.5%
Other values (89) 2238
70.1%
ValueCountFrequency (%)
1 3
 
0.1%
2 2
 
0.1%
3 51
 
1.6%
4 43
 
1.3%
5 110
3.4%
5.5 1
 
< 0.1%
6 148
4.6%
7 79
2.5%
8 63
2.0%
9 67
2.1%
ValueCountFrequency (%)
289 1
 
< 0.1%
124 1
 
< 0.1%
112 1
 
< 0.1%
108 3
0.1%
105 2
0.1%
101 2
0.1%
98 3
0.1%
97 1
 
< 0.1%
87 2
0.1%
81 3
0.1%

total sulfur dioxide
Real number (ℝ)

High correlation 

Distinct228
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.897681
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.9 KiB
2024-10-30T16:33:57.105577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile14
Q138
median88
Q3127
95-th percentile183
Maximum440
Range434
Interquartile range (IQR)89

Descriptive statistics

Standard deviation54.620972
Coefficient of variation (CV)0.62141539
Kurtosis-0.27048547
Mean87.897681
Median Absolute Deviation (MAD)45
Skewness0.41764604
Sum280481.5
Variance2983.4506
MonotonicityNot monotonic
2024-10-30T16:33:57.205540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 41
 
1.3%
28 41
 
1.3%
113 38
 
1.2%
24 36
 
1.1%
15 35
 
1.1%
18 35
 
1.1%
122 34
 
1.1%
23 34
 
1.1%
20 33
 
1.0%
14 33
 
1.0%
Other values (218) 2831
88.7%
ValueCountFrequency (%)
6 3
 
0.1%
7 4
 
0.1%
8 14
 
0.4%
9 15
0.5%
10 28
0.9%
11 26
0.8%
12 29
0.9%
13 28
0.9%
14 33
1.0%
15 35
1.1%
ValueCountFrequency (%)
440 1
< 0.1%
289 1
< 0.1%
278 1
< 0.1%
259 1
< 0.1%
251 1
< 0.1%
248 2
0.1%
243 1
< 0.1%
240 1
< 0.1%
237 2
0.1%
230 1
< 0.1%

density
Real number (ℝ)

High correlation 

Distinct862
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9128882
Minimum0.98711
Maximum100.369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.9 KiB
2024-10-30T16:33:57.307529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.98963
Q10.99259
median0.99551
Q30.99726
95-th percentile0.9994
Maximum100.369
Range99.38189
Interquartile range (IQR)0.00467

Descriptive statistics

Standard deviation8.8018583
Coefficient of variation (CV)4.6013449
Kurtosis118.81552
Mean1.9128882
Median Absolute Deviation (MAD)0.00209
Skewness10.91168
Sum6104.0261
Variance77.472709
MonotonicityNot monotonic
2024-10-30T16:33:57.415657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9976 37
 
1.2%
0.9968 37
 
1.2%
0.9972 37
 
1.2%
0.9984 35
 
1.1%
0.998 32
 
1.0%
0.9964 29
 
0.9%
0.9962 28
 
0.9%
0.9978 27
 
0.8%
0.997 27
 
0.8%
0.9974 25
 
0.8%
Other values (852) 2877
90.2%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98722 1
< 0.1%
0.9874 1
< 0.1%
0.98742 2
0.1%
0.98746 2
0.1%
0.98758 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 2
0.1%
0.98816 1
< 0.1%
ValueCountFrequency (%)
100.369 2
0.1%
100.315 3
0.1%
100.295 2
0.1%
100.289 1
 
< 0.1%
100.242 2
0.1%
100.196 1
 
< 0.1%
100.044 2
0.1%
100.038 2
0.1%
100.037 2
0.1%
100.025 1
 
< 0.1%

pH
Real number (ℝ)

Distinct98
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2363115
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.9 KiB
2024-10-30T16:33:57.521797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile2.97
Q13.12
median3.23
Q33.35
95-th percentile3.52
Maximum4.01
Range1.27
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.16505503
Coefficient of variation (CV)0.05100097
Kurtosis0.36148226
Mean3.2363115
Median Absolute Deviation (MAD)0.11
Skewness0.29060474
Sum10327.07
Variance0.027243162
MonotonicityNot monotonic
2024-10-30T16:33:57.634154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16 99
 
3.1%
3.26 96
 
3.0%
3.22 90
 
2.8%
3.2 88
 
2.8%
3.36 83
 
2.6%
3.24 83
 
2.6%
3.18 82
 
2.6%
3.3 78
 
2.4%
3.14 78
 
2.4%
3.15 78
 
2.4%
Other values (88) 2336
73.2%
ValueCountFrequency (%)
2.74 1
 
< 0.1%
2.79 1
 
< 0.1%
2.8 1
 
< 0.1%
2.82 1
 
< 0.1%
2.83 4
 
0.1%
2.85 3
 
0.1%
2.86 8
0.3%
2.87 4
 
0.1%
2.88 11
0.3%
2.89 5
0.2%
ValueCountFrequency (%)
4.01 2
0.1%
3.9 2
0.1%
3.85 1
 
< 0.1%
3.78 2
0.1%
3.76 1
 
< 0.1%
3.75 3
0.1%
3.74 1
 
< 0.1%
3.72 3
0.1%
3.71 4
0.1%
3.7 1
 
< 0.1%

sulphates
Real number (ℝ)

High correlation 

Distinct110
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57401128
Minimum0.23
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.9 KiB
2024-10-30T16:33:58.229655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile0.36
Q10.47
median0.55
Q30.65
95-th percentile0.86
Maximum2
Range1.77
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.16666078
Coefficient of variation (CV)0.29034409
Kurtosis8.8869131
Mean0.57401128
Median Absolute Deviation (MAD)0.09
Skewness1.874581
Sum1831.67
Variance0.027775817
MonotonicityNot monotonic
2024-10-30T16:33:58.335986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54 126
 
3.9%
0.5 120
 
3.8%
0.56 115
 
3.6%
0.58 102
 
3.2%
0.6 101
 
3.2%
0.52 101
 
3.2%
0.53 101
 
3.2%
0.48 94
 
2.9%
0.57 87
 
2.7%
0.49 86
 
2.7%
Other values (100) 2158
67.6%
ValueCountFrequency (%)
0.23 1
 
< 0.1%
0.25 1
 
< 0.1%
0.26 3
 
0.1%
0.27 6
 
0.2%
0.28 2
 
0.1%
0.29 5
 
0.2%
0.3 9
0.3%
0.31 15
0.5%
0.32 11
0.3%
0.33 17
0.5%
ValueCountFrequency (%)
2 1
 
< 0.1%
1.98 1
 
< 0.1%
1.95 2
0.1%
1.62 1
 
< 0.1%
1.61 1
 
< 0.1%
1.59 1
 
< 0.1%
1.56 1
 
< 0.1%
1.36 3
0.1%
1.34 1
 
< 0.1%
1.33 1
 
< 0.1%

alcohol
Real number (ℝ)

High correlation 

Distinct85
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.630809
Minimum8.4
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.9 KiB
2024-10-30T16:33:58.437908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.1
Q19.5
median10.5
Q311.4
95-th percentile12.8
Maximum14.9
Range6.5
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.2125479
Coefficient of variation (CV)0.1140598
Kurtosis-0.57885207
Mean10.630809
Median Absolute Deviation (MAD)0.95
Skewness0.534312
Sum33922.91
Variance1.4702725
MonotonicityNot monotonic
2024-10-30T16:33:58.538420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 200
 
6.3%
9.4 170
 
5.3%
9.2 131
 
4.1%
11 120
 
3.8%
9.8 117
 
3.7%
10.5 107
 
3.4%
10 98
 
3.1%
11.2 90
 
2.8%
9.6 89
 
2.8%
10.4 87
 
2.7%
Other values (75) 1982
62.1%
ValueCountFrequency (%)
8.4 5
 
0.2%
8.5 5
 
0.2%
8.6 2
 
0.1%
8.7 19
 
0.6%
8.8 33
 
1.0%
8.9 16
 
0.5%
9 68
2.1%
9.05 1
 
< 0.1%
9.1 71
2.2%
9.2 131
4.1%
ValueCountFrequency (%)
14.9 1
 
< 0.1%
14.2 1
 
< 0.1%
14.05 1
 
< 0.1%
14 9
0.3%
13.9 2
 
0.1%
13.8 2
 
0.1%
13.7 3
 
0.1%
13.6 13
0.4%
13.55 1
 
< 0.1%
13.5 6
0.2%

quality
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7828267
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.9 KiB
2024-10-30T16:33:58.625126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.83054896
Coefficient of variation (CV)0.14362335
Kurtosis0.23399302
Mean5.7828267
Median Absolute Deviation (MAD)1
Skewness0.16061177
Sum18453
Variance0.68981157
MonotonicityNot monotonic
2024-10-30T16:33:58.703682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 1432
44.9%
5 1094
34.3%
7 491
 
15.4%
4 92
 
2.9%
8 68
 
2.1%
3 14
 
0.4%
ValueCountFrequency (%)
3 14
 
0.4%
4 92
 
2.9%
5 1094
34.3%
6 1432
44.9%
7 491
 
15.4%
8 68
 
2.1%
ValueCountFrequency (%)
8 68
 
2.1%
7 491
 
15.4%
6 1432
44.9%
5 1094
34.3%
4 92
 
2.9%
3 14
 
0.4%

Interactions

2024-10-30T16:33:54.431915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:42.603869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:44.176841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:45.159037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:46.119500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:47.056432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:48.346824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:49.306291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:50.227486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:51.157768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:52.520418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:53.499817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:54.505400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:42.681445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:44.262983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:45.238416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:46.198601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:47.475269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:48.427099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:49.381980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:50.302880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:51.235230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:52.598781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:53.578080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:54.585330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:43.372837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:44.349991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:45.320485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:46.282969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:47.564360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:48.510092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:49.462609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:50.386058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:51.320273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:52.685003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:53.657718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:54.657549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:43.448605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:44.425822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:45.399287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:46.360787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:47.641971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:48.587635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:49.536596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:50.464117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:51.401025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:52.767235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:53.733665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:54.729613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:43.527588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:44.509393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:45.479673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:46.436779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:47.718603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:48.663695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:49.614419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:50.539858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:51.477490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:52.844791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:53.809099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:54.805243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:43.608995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:44.591524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:45.557984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:46.514189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:47.797431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:48.745117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:49.690562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:50.620214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:51.559679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:52.927671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:53.885426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:54.885928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:43.692012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:44.672895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:45.636417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:46.592160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:47.876858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:48.833741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:49.767343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:50.699144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:51.640512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:53.008041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:53.963369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:54.955223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:43.767827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:44.746773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:45.710947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:46.662920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:47.952126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:48.908748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:49.839394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:50.771133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:51.715612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:53.084807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:54.038252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:55.028233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:43.852569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:44.828797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:45.788988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:46.735417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:48.028477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:48.985505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:49.914255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:50.847504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:51.790168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:53.173681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:54.113737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:55.104136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:43.933466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:44.914235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:45.879430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:46.816679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:48.107591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:49.062836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:49.992199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:50.923030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:51.868484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:53.259373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:54.193390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:55.184701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:44.018060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:44.996228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:45.963430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:46.900359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:48.190622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:49.147642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:50.074322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:51.003633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:51.952306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:53.342238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:54.282045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:55.258562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:44.100145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:45.079597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:46.041533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:46.980484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:48.270098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:49.226678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:50.152802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:51.080976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:52.031493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:53.422024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T16:33:54.359081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-30T16:33:58.773018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
alcoholchloridescitric aciddensityfixed acidityfree sulfur dioxidepHqualityresidual sugarsulphatestotal sulfur dioxidetypevolatile acidity
alcohol1.000-0.3990.087-0.653-0.208-0.0280.1160.475-0.161-0.016-0.0990.257-0.139
chlorides-0.3991.000-0.0450.6530.579-0.4440.257-0.311-0.1760.457-0.5190.7420.576
citric acid0.087-0.0451.0000.0860.2860.068-0.3510.1740.0740.1430.1110.486-0.401
density-0.6530.6530.0861.0000.608-0.2540.072-0.2990.2740.395-0.2730.0000.376
fixed acidity-0.2080.5790.2860.6081.000-0.426-0.125-0.118-0.1190.409-0.4740.6000.336
free sulfur dioxide-0.028-0.4440.068-0.254-0.4261.000-0.2630.1090.330-0.3180.8010.495-0.449
pH0.1160.257-0.3510.072-0.125-0.2631.000-0.074-0.2870.276-0.3830.4650.416
quality0.475-0.3110.174-0.299-0.1180.109-0.0741.0000.0650.0460.0150.192-0.341
residual sugar-0.161-0.1760.0740.274-0.1190.330-0.2870.0651.000-0.1840.4230.551-0.188
sulphates-0.0160.4570.1430.3950.409-0.3180.2760.046-0.1841.000-0.4230.5220.302
total sulfur dioxide-0.099-0.5190.111-0.273-0.4740.801-0.3830.0150.423-0.4231.0000.794-0.497
type0.2570.7420.4860.0000.6000.4950.4650.1920.5510.5220.7941.0000.683
volatile acidity-0.1390.576-0.4010.3760.336-0.4490.416-0.341-0.1880.302-0.4970.6831.000

Missing values

2024-10-30T16:33:55.360686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-30T16:33:55.509448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
0Moscatel8.10.240.3210.50.03034.0105.00.994073.110.4211.86
1Moscatel5.80.230.202.00.04339.0154.00.992263.210.3910.26
2Moscatel7.50.330.362.60.05126.0126.00.990973.320.5312.76
3Moscatel6.60.380.369.20.06142.0214.00.997603.310.569.45
4Moscatel6.40.150.291.80.04421.0115.00.991663.100.3810.25
5Moscatel6.50.320.345.70.04427.091.00.991843.280.6012.07
6Moscatel7.50.220.322.40.04529.0100.00.991353.080.6011.37
7Moscatel6.40.230.321.90.03840.0118.00.990743.320.5311.87
8Moscatel6.10.220.311.40.03940.0129.00.991933.450.5910.95
9Moscatel6.50.480.020.90.04332.099.00.992263.140.479.84
typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
3221Syrah6.60.7250.207.80.07329.079.00.997703.290.549.25
3222Syrah6.30.5500.151.80.07726.035.00.993143.320.8211.66
3223Syrah5.40.7400.091.70.08916.026.00.994023.670.5611.66
3224Syrah6.30.5100.132.30.07629.040.00.995743.420.7511.06
3225Syrah6.80.6200.081.90.06828.038.00.996513.420.829.56
3226Syrah6.20.6000.082.00.09032.044.00.994903.450.5810.55
3227Syrah5.90.5500.102.20.06239.051.00.995123.520.7611.26
3228Syrah6.30.5100.132.30.07629.040.00.995743.420.7511.06
3229Syrah5.90.6450.122.00.07532.044.00.995473.570.7110.25
3230Syrah6.00.3100.473.60.06718.042.00.995493.390.6611.06

Duplicate rows

Most frequently occurring

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
192Moscatel7.00.150.2814.700.05129.0149.00.997922.960.399.078
227Moscatel7.30.190.2713.900.05745.0155.00.998072.940.418.888
233Moscatel7.40.160.3013.700.05633.0168.00.998252.900.448.777
232Moscatel7.40.160.2715.500.05025.0135.00.998402.900.438.776
12Moscatel5.70.220.2016.000.04441.0113.00.998623.220.468.965
123Moscatel6.60.220.2317.300.04737.0118.00.999063.080.468.865
140Moscatel6.70.160.3212.500.03518.0156.00.996662.880.369.065
239Moscatel7.50.240.3113.100.05026.0180.00.998843.050.539.165
13Moscatel5.70.220.2216.650.04439.0110.00.998553.240.489.064
27Moscatel6.00.200.266.800.04922.093.00.992803.150.4211.064